SubmissionNumber#=%=#8
FinalPaperTitle#=%=#Multilingual Identification of English Code-Switching
ShortPaperTitle#=%=#
NumberOfPages#=%=#11
CopyrightSigned#=%=#
JobTitle#==#
Organization#==#
Abstract#==#This work addresses the task of identifying English code-switching in multilingual text. We train two token-level classifiers on data of high-resource language pairs. The first distinguishes between English, not English, morphologically mixed, and other words. The second is a binary classifier that identifies named entities. Results indicate that our system is on-par with SoTA for high-resource language pairs. Meanwhile we show that on low-resource language pairs not in the training data our system outperforms SoTA by between 2.31 and 4.59\% $F_1$. We also analyse the correlation between typological similarity of the languages and difficulty in recognizing code-switching. Our system is a new strong baseline system for code-switching research between any language and English.
Author{1}{Firstname}#=%=#Igor
Author{1}{Lastname}#=%=#Sterner
Author{1}{Username}#=%=#igorsterner
Author{1}{Email}#=%=#is473@cam.ac.uk
Author{1}{Affiliation}#=%=#University of Cambridge

==========
èéáğö